{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:22:24.427031Z",
     "start_time": "2020-01-07T13:22:14.107394Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-06T14:02:03.089075Z",
     "start_time": "2020-01-06T14:02:03.064825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.13.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:26:58.271961Z",
     "start_time": "2020-01-07T13:26:49.742416Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import numpy as py \n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载mnist数据集到当前目录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:36:17.286636Z",
     "start_time": "2020-01-07T13:36:16.888266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./train-images-idx3-ubyte.gz\n",
      "Extracting ./train-labels-idx1-ubyte.gz\n",
      "Extracting ./t10k-images-idx3-ubyte.gz\n",
      "Extracting ./t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看训练集图片和标签的维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:36:19.625494Z",
     "start_time": "2020-01-07T13:36:19.615857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784)\n",
      "(55000,)\n"
     ]
    }
   ],
   "source": [
    "print(mnist.train.images.shape)\n",
    "print(mnist.train.labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出mnist数据集中训练数据的前两行标签，未使用one_hot编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:36:29.702950Z",
     "start_time": "2020-01-07T13:36:29.658712Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 3]\n"
     ]
    }
   ],
   "source": [
    "print(mnist.train.labels[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 看一看训练集中图片和标签的样子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:36:43.201232Z",
     "start_time": "2020-01-07T13:36:42.811476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "for i in range(16):\n",
    "    plt.subplot(4,4,i+1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"label: {}\".format(mnist.train.labels[i]))\n",
    "    plt.imshow(mnist.train.images[i].reshape(28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建立模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:02:02.539654Z",
     "start_time": "2020-01-07T14:02:02.505053Z"
    }
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 784], name=\"x\")\n",
    "y = tf.placeholder(tf.int64, [None], name=\"y\")\n",
    "\n",
    "\n",
    "l1_units_count =300 # 增加神经元\n",
    "W_1= tf.Variable(tf.truncated_normal([784,l1_units_count],stddev = 0.1))\n",
    "b_1 = tf.Variable(tf.truncated_normal([l1_units_count],stddev = 0.1))\n",
    "logits_1 = tf.matmul(x,W_1) +b_1\n",
    "output_1 = tf.nn.relu(logits_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T13:51:32.394166Z",
     "start_time": "2020-01-07T13:51:32.380715Z"
    }
   },
   "source": [
    "隐层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:02:32.629909Z",
     "start_time": "2020-01-07T14:02:32.607521Z"
    }
   },
   "outputs": [],
   "source": [
    "l2_units_count=100\n",
    "W_2= tf.Variable(tf.truncated_normal([l1_units_count,l2_units_count],stddev = 0.1))\n",
    "b_2 = tf.Variable(tf.truncated_normal([l2_units_count],stddev = 0.1))\n",
    "logits_2 = tf.matmul(output_1,W_2) +b_2\n",
    "output_2 = tf.nn.relu(logits_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:04:51.221513Z",
     "start_time": "2020-01-07T14:04:51.194191Z"
    }
   },
   "outputs": [],
   "source": [
    "l3_units_count=10\n",
    "W_3= tf.Variable(tf.truncated_normal([l2_units_count,l3_units_count],stddev = 0.1))\n",
    "b_3 = tf.Variable(tf.truncated_normal([l3_units_count],stddev = 0.1))\n",
    "logits_3 = tf.matmul(output_2,W_3) +b_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义正则项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:14:36.655093Z",
     "start_time": "2020-01-07T14:14:36.644133Z"
    }
   },
   "outputs": [],
   "source": [
    "regularization_rate= 0.0001\n",
    "regularizer = tf.contrib.layers.l2_regularizer(regularization_rate)\n",
    "regularization= regularizer(W_1) + regularizer(W_2) + regularizer(W_3) # L2正则项"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义优化算法，训练步长，计算正确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:33:21.094066Z",
     "start_time": "2020-01-07T14:33:20.996120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred Tensor(\"Softmax_4:0\", shape=(?, 10), dtype=float32)\n",
      "y Tensor(\"y_8:0\", shape=(?,), dtype=int64)\n",
      "correct_pred Tensor(\"Equal_4:0\", shape=(?,), dtype=bool)\n"
     ]
    }
   ],
   "source": [
    "global_step = tf.Variable(0,trainable = False)  \n",
    "# 设置学习率衰减\n",
    "lr= tf.train.exponential_decay(0.8,global_step,100,0.99,staircase=True) \n",
    "#损失\n",
    "cross_entropy_loss=tf.reduce_mean(\n",
    "tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_3, labels=y))+regularization \n",
    "#梯度下降优化\n",
    "# optimizer=tf.train.GradientDescentOptimizer(\n",
    "# learning_rate=lr).minimize(cross_entropy_loss,global_step=global_step) \n",
    "optimizer=tf.train.GradientDescentOptimizer(\n",
    "learning_rate=lr).minimize(cross_entropy_loss,global_step=global_step) \n",
    "\n",
    "pred=tf.nn.softmax(logits_3)\n",
    "correct_pred=tf.equal(tf.argmax(pred,1),y)\n",
    "#计算准确率\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n",
    "\n",
    "print(\"pred\",pred)\n",
    "print(\"y\",y)\n",
    "print(\"correct_pred\",correct_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获得计算图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:17:43.611412Z",
     "start_time": "2020-01-07T14:17:43.605998Z"
    }
   },
   "outputs": [],
   "source": [
    "grap = tf.get_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:17:53.686121Z",
     "start_time": "2020-01-07T14:17:53.681592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.framework.ops.Graph at 0x7f7766050be0>"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow有tf.Graph类，用于存储计算图。而计算图其实就是由节点和有向边组成，每个点包括操作Op、数值value、类型dtype、形状shape等属性。探索诸如tf.Variable()等函数的内部机制的过程中，就需要查看计算图的变化情况，包括新建了哪些节点，输入是什么等等\n",
    "\n",
    "例如: name = \"x\" 的节点，有操作PlaceHolder，有float的属性和784维的属性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:33:26.785995Z",
     "start_time": "2020-01-07T14:33:26.727818Z"
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     "execution_count": 51,
     "metadata": {},
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   "source": [
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  {
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   "metadata": {},
   "source": [
    "现在我们可以在一个Session里面启动我们的模型，并且初始化变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:33:29.873885Z",
     "start_time": "2020-01-07T14:33:29.862651Z"
    }
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:33:32.005231Z",
     "start_time": "2020-01-07T14:33:31.917558Z"
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   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T14:38:32.341533Z",
     "start_time": "2020-01-07T14:38:06.282619Z"
    }
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "####################################################################################################\n",
      "step [100], entropy loss: [0.05790265277028084]\n",
      "1.0\n",
      "0.9824\n",
      "####################################################################################################\n",
      "step [200], entropy loss: [0.060768917202949524]\n",
      "1.0\n",
      "0.9828\n",
      "####################################################################################################\n",
      "step [300], entropy loss: [0.05569863319396973]\n",
      "1.0\n",
      "0.9824\n",
      "####################################################################################################\n",
      "step [400], entropy loss: [0.05467308685183525]\n",
      "1.0\n",
      "0.9827\n",
      "####################################################################################################\n",
      "step [500], entropy loss: [0.05836103856563568]\n",
      "1.0\n",
      "0.9824\n",
      "####################################################################################################\n",
      "step [600], entropy loss: [0.05341595038771629]\n",
      "1.0\n",
      "0.983\n",
      "####################################################################################################\n",
      "step [700], entropy loss: [0.05580146983265877]\n",
      "1.0\n",
      "0.9825\n",
      "####################################################################################################\n",
      "step [800], entropy loss: [0.055867575109004974]\n",
      "1.0\n",
      "0.9823\n",
      "####################################################################################################\n",
      "step [900], entropy loss: [0.05485527589917183]\n",
      "1.0\n",
      "0.9825\n",
      "####################################################################################################\n",
      "step [1000], entropy loss: [0.05411754548549652]\n",
      "1.0\n",
      "0.9824\n",
      "####################################################################################################\n",
      "step [1100], entropy loss: [0.05424277111887932]\n",
      "1.0\n",
      "0.9827\n",
      "####################################################################################################\n",
      "step [1200], entropy loss: [0.05355418473482132]\n",
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      "step [1300], entropy loss: [0.053994111716747284]\n",
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      "step [1500], entropy loss: [0.053512103855609894]\n",
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      "step [1800], entropy loss: [0.0543525293469429]\n",
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      "step [3900], entropy loss: [0.05207338184118271]\n",
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      "step [4000], entropy loss: [0.05138102546334267]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################################################################################################\n",
      "step [5200], entropy loss: [0.048987001180648804]\n",
      "1.0\n",
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      "step [5700], entropy loss: [0.04843461140990257]\n",
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      "step [5800], entropy loss: [0.04839742183685303]\n",
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      "####################################################################################################\n",
      "step [5900], entropy loss: [0.047183431684970856]\n",
      "1.0\n",
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      "####################################################################################################\n",
      "step [6000], entropy loss: [0.049347132444381714]\n",
      "1.0\n",
      "0.9824\n"
     ]
    }
   ],
   "source": [
    "saver=tf.train.Saver()\n",
    "for step in range(6000):\n",
    "    batch_x, batch_y = mnist.train.next_batch(100)\n",
    "    _, loss = sess.run([optimizer, cross_entropy_loss],\n",
    "                       feed_dict={\n",
    "                           x: batch_x,\n",
    "                           y: batch_y\n",
    "                       })\n",
    "    if (step + 1) % 100 == 0:\n",
    "        print(\"#\" * 100)\n",
    "        print(\"step [{}], entropy loss: [{}]\".format(step + 1, loss))\n",
    "        #打印训练集上的正确率\n",
    "        print(sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}))\n",
    "        #打印测试集上的正确率\n",
    "        print(\n",
    "            sess.run(accuracy,\n",
    "                     feed_dict={\n",
    "                         x: mnist.test.images,\n",
    "                         y: mnist.test.labels\n",
    "                     }))\n",
    "        # 存储模型到制定的路径\n",
    "        saver.save(sess, './model.ckpt', global_step=i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用图表看下结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-01-07T15:13:31.738111Z",
     "start_time": "2020-01-07T15:13:31.141291Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./model.ckpt-15\n",
      "final_pred [[7.14383619e-09 1.70700758e-07 4.65785099e-08 1.56155863e-06\n",
      "  2.84106316e-09 7.53537055e-09 2.00551672e-10 9.99997377e-01\n",
      "  2.56151256e-09 7.96697179e-07]\n",
      " [3.34071604e-10 1.58931869e-06 9.99998212e-01 2.83528721e-07\n",
      "  1.64627374e-11 1.85850831e-11 3.97987199e-09 1.16499907e-10\n",
      "  1.06768683e-08 2.13395094e-14]\n",
      " [1.09347219e-07 9.99765933e-01 7.50452818e-05 5.20783345e-08\n",
      "  2.53606904e-05 6.06627964e-07 3.84538089e-06 7.77437817e-05\n",
      "  5.12994266e-05 7.56364997e-08]\n",
      " [9.99992728e-01 1.32286772e-07 1.75132300e-06 1.41282142e-08\n",
      "  2.33402659e-07 1.20263515e-08 4.10090706e-06 7.05398634e-07\n",
      "  5.20653576e-10 3.93011277e-07]\n",
      " [4.21713867e-06 8.23059622e-07 1.26232135e-06 1.75646420e-07\n",
      "  9.99675393e-01 3.94963308e-07 8.34765387e-07 6.59346915e-05\n",
      "  2.85871124e-07 2.50781071e-04]\n",
      " [2.43230969e-09 9.99962330e-01 1.06120660e-06 3.85872845e-09\n",
      "  6.22728612e-06 1.43876493e-08 5.39360094e-08 2.87813928e-05\n",
      "  1.50174947e-06 9.51536983e-09]\n",
      " [2.49905057e-08 6.28148555e-08 2.03088945e-07 3.19215445e-08\n",
      "  9.99752462e-01 1.20181030e-05 3.25242695e-08 8.11595055e-06\n",
      "  1.24653263e-04 1.02383958e-04]\n",
      " [7.74730438e-07 2.09338964e-07 1.23990449e-05 5.56299090e-03\n",
      "  1.79247272e-05 1.02207832e-05 4.18544950e-08 8.78924184e-05\n",
      "  7.13554891e-06 9.94300485e-01]\n",
      " [2.19407821e-06 4.78173909e-08 5.41334293e-06 3.79047577e-07\n",
      "  2.11159186e-05 9.18052852e-01 7.86635876e-02 3.43663054e-08\n",
      "  3.17665632e-03 7.77195382e-05]\n",
      " [9.08858588e-09 4.17659720e-08 1.52203661e-09 1.01090052e-06\n",
      "  4.20073140e-03 5.17384322e-08 1.74016079e-09 4.01676116e-05\n",
      "  3.97545637e-06 9.95754123e-01]\n",
      " [9.99993086e-01 9.62491153e-09 2.49485811e-06 2.13713069e-09\n",
      "  1.52820192e-08 7.65134587e-07 5.82666871e-07 2.49432992e-06\n",
      "  8.72558137e-10 5.50952848e-07]\n",
      " [2.60370089e-05 2.48095837e-08 4.71322778e-07 2.09089084e-08\n",
      "  9.90391342e-08 6.24241147e-05 9.99892831e-01 2.20541008e-09\n",
      "  1.79866838e-05 4.12589642e-08]\n",
      " [5.97990324e-10 1.49296620e-09 8.34588371e-11 2.61224329e-07\n",
      "  6.14550254e-06 2.78447754e-08 1.67569032e-11 3.14230171e-08\n",
      "  3.14576205e-08 9.99993563e-01]\n",
      " [9.99999285e-01 4.35908115e-10 4.78544671e-08 2.02373826e-10\n",
      "  1.45716661e-09 2.15880540e-08 3.51875471e-08 2.28367540e-07\n",
      "  2.79689799e-10 4.45241170e-07]\n",
      " [1.93185953e-11 9.99999404e-01 1.60617342e-09 6.63867823e-08\n",
      "  3.90965091e-08 2.59684829e-09 6.25432639e-09 1.22350823e-08\n",
      "  3.71434396e-07 1.25277777e-09]\n",
      " [4.23979829e-09 2.49192258e-08 3.18652482e-08 4.56924434e-04\n",
      "  9.11603903e-10 9.99541759e-01 4.34636718e-08 1.81987325e-09\n",
      "  1.24053520e-06 8.67228707e-08]]\n",
      "acc 1.0\n",
      "orders [[6 8 4 0 5 2 1 9 3 7]\n",
      " [9 4 5 7 0 6 8 3 1 2]\n",
      " [3 9 0 5 6 4 8 2 7 1]\n",
      " [8 5 3 1 4 9 7 2 6 0]\n",
      " [3 8 5 1 6 2 0 7 9 4]\n",
      " [0 3 9 5 6 2 8 4 7 1]\n",
      " [0 3 6 1 2 7 5 9 8 4]\n",
      " [6 1 0 8 5 2 4 7 3 9]\n",
      " [7 1 3 0 2 4 9 8 6 5]\n",
      " [2 6 0 1 5 3 8 7 4 9]\n",
      " [8 3 1 4 9 6 5 7 2 0]\n",
      " [7 3 1 9 4 2 8 0 5 6]\n",
      " [6 2 0 1 5 7 8 3 4 9]\n",
      " [3 8 1 4 5 6 2 7 9 0]\n",
      " [0 9 2 5 6 7 4 3 8 1]\n",
      " [4 7 0 1 2 6 9 8 3 5]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "with tf.Session() as sess:\n",
    "    ckpt = tf.train.get_checkpoint_state('.')\n",
    "    # 如果ckpt 和 ckpt.model_checkpoint_path 都存在\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        # 从指定路径中恢复模型\n",
    "        saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "        #取测试集中16个数据的预测结果\n",
    "        final_pred, acc = sess.run([pred, accuracy],\n",
    "                                   feed_dict={\n",
    "                                       x: mnist.test.images[:16],\n",
    "                                       y: mnist.test.labels[:16]\n",
    "                                   })\n",
    "        # 将预测的概率由小到大排列存入orders列表，最后一个的概率最大，所以最后一个值即是预测的值\n",
    "        orders = np.argsort(final_pred)\n",
    "        \n",
    "        print(\"final_pred\",final_pred)\n",
    "        print(\"acc\",acc)\n",
    "        print(\"orders\",orders)\n",
    "        \n",
    "        plt.figure(figsize=(11, 11))\n",
    "        for idx in range(16):\n",
    "            # 取orders中最后一个值，即预测值\n",
    "            order = orders[idx, :][-1]\n",
    "            # 取预测值出现的概率\n",
    "            prob = final_pred[idx, :][order]\n",
    "            plt.subplot(4, 4, idx + 1)\n",
    "            plt.axis('off')\n",
    "            plt.title('label:{}-pred:{}-{:.1f}%'.format(mnist.test.labels[idx],\n",
    "                                                  order, prob * 100))\n",
    "            plt.imshow(mnist.test.images[idx].reshape((28, 28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ⼼得体会"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1、如何修改隐层数量，修改后会起到什么样的效果10分。\n",
    "\n",
    "修改隐曾数量即加入更多层的神经网络，具体修改方法看上面的建立模型处;\n",
    "增加模型的隐层后，模型的复杂度会增加，有助于更好的拟合数据。\n",
    "若隐层节点数太少，网络可能根本不能训练或网络性能很差；若隐层节点数太多，虽然可使网络的系统误差减小，但一方面使网络训练时间延长，另一方面，训练容易出现“过拟合”。因此，合理隐层节点数应在综合考虑网络结构复杂程度和误差大小的情况下来确定。\n",
    "\n",
    "\n",
    "2、如何修改神经元个数，起到了什么样的效果10分。\n",
    "\n",
    "具体修改的方法见上面的建立模型处;\n",
    "修改神经元的个数增加了权重在行方向的维度，在上面的例子中我将第一层的神经元的个数增加到了300,第二层是100，第3层是10。\n",
    "增加神经元的个数也有助于更好的拟合数据，同样的，过多的神经元个数也会使网络训练时间延长。\n",
    "\n",
    "\n",
    "3、如何在模型中添加L1/L2正则化，正则化起什么作⽤10分。\n",
    "\n",
    "具体添加的方法见上面的建立模型处;正则化可以有效的防止模型出现过拟合。\n",
    "\n",
    "\n",
    "4、使⽤不同的初始化⽅式对模型有什么影响10分。\n",
    "\n",
    "不同的初始化方法，对神经网络的训练结果影响很大，选对初始化方法可以大大提高神经网络的准确率，上面的例子中，我使用截断的标准差为0.1的正态分布中的随机值来初始化权重w和偏置b。\n",
    "\n",
    "最原始的初始化：全部为固定值\n",
    "稍好些的初始化：服从固定方差的独立高斯分布，但是随着层级的加深，高斯分布初始化的参数容易出现过大或过小的问题\n",
    "解决方法：Xavier初始化或MSRA初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
